Bibliographic record
Abstract
This paper examines how well forecasters can predict the future time path of (policy-determined) short-term interest rates. Most prior work has been done using U.S. data; in this exercise we use forecasts made for New Zealand by the Reserve Bank of New Zealand (RBNZ) and those derived from money market yield curves in the United Kingdom. We broadly replicate recent U.S. findings for New Zealand and the United Kingdom, to show that such forecasts in New Zealand and the United Kingdom have been excellent for the immediate forthcoming quarter, reasonable for the next quarter, and useless thereafter. Moreover, when ex post errors are assessed depending on whether interest rates have been in an upward, or downward, section of the cycle, they are shown to have been biased and, apparently, inefficient. We attempt to explain those findings, and examine whether the apparent ex post forecast inefficiencies may still be consistent with ex ante forecast efficiency. We conclude, first, that the best forecast may be a hybrid containing a specific forecast for the next six months and a “no-change ” assumption thereafter, and, second, that the modal forecast for interest rates, and maybe for other variables as well, is skewed, generally underestimating the likely continuation of the current phase of the cycle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".